Bench / Methodology / run ad86ac26-016c-4117-82e3-f1272c639a95
Tone-matched support reply
openai/gpt-4o-mini · task v1 · methodology v1 · visitor-key / video-demo
LLM judge
As of .
Candidate cost: 0.00005445
Judge cost: 0.0000554
Total (candidate + judge): 0.00010985
Cost source: openrouter_usage
Tokens in / out: 51 / 78
Latency: 535 ms (wall clock)
Stored at:
Comparison: 5f523c95-41ba-4957-bef4-4d87199e25ed
Amounts are the exact stored decimal strings. No rounding is applied. candidate_cost_usd is the sum of measured run costs. judge_cost_usd is the sum of stored tier-2 judge ledger rows, or null when none were recorded. total_cost_usd equals candidate plus judge only when every candidate cost is present and a judge cost was recorded.
Derived integrity
These fields are computed from the stored output and evaluation. The output text below is unchanged.
output_incomplete: false
completeness_confidence: high
completeness_reason: —
presentation: flawless
Spot check: Human spot check
Spot-check note: Pass: apologizes for cracked mug and offers replacement shipment; stays on replacement path. Excerpt: "Dear [Customer's Name], I’m so sorry to hear that your mug arrived cracked. We want to make this rig…"
Output
Dear [Customer's Name], I’m so sorry to hear that your mug arrived cracked. We want to make this right for you! I can offer you a replacement shipment at no additional cost. Please let me know if that works for you, and I’ll get it processed right away. Warm regards, [Your Name] [Your Position] [Your Company]
Evaluation stored on this run
{
"tier": 2,
"notes": "The reply is warm, brief, and direct, offers a replacement, and clearly outlines the next steps without promising a refund.",
"score": 5,
"tier3": {
"at": "2026-10-02T00:49:53.256Z",
"date": "2026-10-02",
"note": "Pass: apologizes for cracked mug and offers replacement shipment; stays on replacement path. Excerpt: \"Dear [Customer's Name], I’m so sorry to hear that your mug arrived cracked. We want to make this rig…\"",
"passed": true,
"verdict": "agree",
"reviewer": "cos",
"issue_found": false,
"output_read": true
},
"passed": true,
"status": "scored",
"dimensions": {
"accuracy": 5,
"tone_match": 5,
"completeness": 5,
"policy_safety": 5
},
"tier3_kind": "human",
"tier_label": "LLM judge",
"tier3_label": "Human spot check",
"presentation": "flawless",
"score_capped": false,
"judge_model_id": "google/gemini-2.5-flash-lite",
"display_overall": 5,
"output_incomplete": false,
"completeness_reason": null,
"rubric_prompt_version": "rubric-v1",
"completeness_confidence": "high"
}